Zobrazeno 1 - 10
of 57 243
pro vyhledávání: '"user interactions"'
Social media platforms depend on algorithms to select, curate, and deliver content personalized for their users. These algorithms leverage users' past interactions and extensive content libraries to retrieve and rank content that personalizes experie
Externí odkaz:
http://arxiv.org/abs/2407.07227
Autor:
Jeong, Dong Hyun1 (AUTHOR) djeong@udc.edu, Jeong, Bong Keun2 (AUTHOR) bjeong@coastal.edu, Ji, Soo Yeon3 (AUTHOR) djeong@udc.edu
Publikováno v:
Information (2078-2489). Jun2024, Vol. 15 Issue 6, p351. 22p.
Autor:
Shi, Taiwei, Wang, Zhuoer, Yang, Longqi, Lin, Ying-Chun, He, Zexue, Wan, Mengting, Zhou, Pei, Jauhar, Sujay, Xu, Xiaofeng, Song, Xia, Neville, Jennifer
As large language models (LLMs) continue to advance, aligning these models with human preferences has emerged as a critical challenge. Traditional alignment methods, relying on human or LLM annotated datasets, are limited by their resource-intensive
Externí odkaz:
http://arxiv.org/abs/2408.15549
This paper aims to investigate the impact of interference in social network algorithms via user-bot interactions, focusing on the Stochastic Bounded Confidence Model (SBCM). This paper explores two approaches: positioning bots controlled by agents in
Externí odkaz:
http://arxiv.org/abs/2409.11426
Our study presents a multifaceted approach to enhancing user interaction and content relevance in social media platforms through a federated learning framework. We introduce personalized GPT and Context-based Social Media LLM models, utilizing federa
Externí odkaz:
http://arxiv.org/abs/2408.05243
Autor:
Vujasinović, Stéphane, Becker, Stefan, Bullinger, Sebastian, Scherer-Negenborn, Norbert, Arens, Michael, Stiefelhagen, Rainer
In this paper, we introduce a variant of video object segmentation (VOS) that bridges interactive and semi-automatic approaches, termed Lazy Video Object Segmentation (ziVOS). In contrast, to both tasks, which handle video object segmentation in an o
Externí odkaz:
http://arxiv.org/abs/2408.00169
While recommender systems with multi-modal item representations (image, audio, and text), have been widely explored, learning recommendations from multi-modal user interactions (e.g., clicks and speech) remains an open problem. We study the case of m
Externí odkaz:
http://arxiv.org/abs/2405.04246
Autor:
Morales-Garzón, Andrea1 (AUTHOR) amoralesg@decsai.ugr.es, Gutiérrez-Batista, Karel1 (AUTHOR), Martin-Bautista, Maria J.1 (AUTHOR)
Publikováno v:
Computing. Jul2024, Vol. 106 Issue 7, p2133-2155. 23p.
This study investigates the capacity of Large Language Models (LLMs) to infer the Big Five personality traits from free-form user interactions. The results demonstrate that a chatbot powered by GPT-4 can infer personality with moderate accuracy, outp
Externí odkaz:
http://arxiv.org/abs/2405.13052
Publikováno v:
Virtual Worlds, Vol 3, Iss 3, Pp 333-353 (2024)
In an increasingly globalized world, the development of language skills and intercultural empathy has become crucial for effective communication and collaboration across diverse societies. Virtual worlds offer a unique and immersive environment to ad
Externí odkaz:
https://doaj.org/article/a2861ad172154eedb9c7856ea1bb92fe